Learning n-tuple networks for othello by coevolutionary gradient search

@inproceedings{Krawiec2011LearningNN,
  title={Learning n-tuple networks for othello by coevolutionary gradient search},
  author={Krzysztof Krawiec and Marcin Grzegorz Szubert},
  booktitle={GECCO},
  year={2011}
}
We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning algorithms that combine elements of local search and population-based search. The approach is applied to learning Othello strategies represented as n-tuple networks, using different search operators and modes of learning. We focus on the interplay between the continuous, directed, gradient-based search in the space of weights, and fitness-driven, combinatorial, coevolutionary search in the space of entire… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-5 of 5 references

Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function

IEEE Transactions on Computational Intelligence and AI in Games • 2010
View 7 Excerpts
Highly Influenced

Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation

2006 IEEE Symposium on Computational Intelligence and Games • 2006
View 5 Excerpts
Highly Influenced

Pattern recognition and reading by machine

IRE-AIEE-ACM '59 (Eastern) • 1959
View 3 Excerpts
Highly Influenced

Temporal Difference Learning and TD-Gammon

ICGA Journal • 1995
View 3 Excerpts
Highly Influenced

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